| Summary: | Aiming at the problems of rolling bearing vibration signals were dif ficult to identify due to strong time-varying and strong noisy characteristics, a method based on adaptive wavelet analysis (AWA) and multiple layers convolution extreme learning auto-encoder (MLCELAE) was proposed. Firstly, a new method was proposed to detect the vibration signals spectrum boundary of rolling bearing, which could divide the signals spectrum adaptively, and then decompose the vibration signals into several intrinsic modal components. Secondly, the components which could best reflect the fault characteristics of the vibration signals were screened and reconstructed. Finally, the convolution extreme learning auto-encoder was constructed and multiple layers convolution extreme learning auto-encoder was built by stacking layer by layer, then the vibration signals samples of rolling bearing were fed into MLCELAE for automatic feature learning and fault identi fication. The experimental results show that the average fault identification accuracy of the proposed method reaches 98.48% and the standard deviation is only 0.17. Compared with other methods, it has more advantages in fault identi fication accuracy of rolling bearing, which is suitable for automatic identi fication of rolling bearing faults.
|
|---|
